Yan Dengying, Zheng Qiguang, Chang Kai, Hua Rui, Liu Yiming, Xue Jingyan, Shu Zixin, Hu Yunhui, Yang Pengcheng, Wei Yu, Lang Jidong, Yu Haibin, Li Xiaodong, Zhang Runshun, Wang Wenjia, Liu Baoyan, Zhou Xuezhong
Institute of Medical Intelligence, School of Computer Science & Technology, Beijing Jiaotong University, Beijing 100044, China.
Institute of Liver Diseases, Hubei Provincial Hospital of Traditional Chinese Medicine, Wuhan, Hubei 430061, China.
Chin J Nat Med. 2025 Nov;23(11):1310-1328. doi: 10.1016/S1875-5364(25)60983-6.
Traditional Chinese medicine (TCM) represents a paradigmatic approach to personalized medicine, developed through the systematic accumulation and refinement of clinical empirical data over more than 2000 years, and now encompasses large-scale electronic medical records (EMR) and experimental molecular data. Artificial intelligence (AI) has demonstrated its utility in medicine through the development of various expert systems (e.g., MYCIN) since the 1970s. With the emergence of deep learning and large language models (LLMs), AI's potential in medicine shows considerable promise. Consequently, the integration of AI and TCM from both clinical and scientific perspectives presents a fundamental and promising research direction. This survey provides an insightful overview of TCM AI research, summarizing related research tasks from three perspectives: systems-level biological mechanism elucidation, real-world clinical evidence inference, and personalized clinical decision support. The review highlights representative AI methodologies alongside their applications in both TCM scientific inquiry and clinical practice. To critically assess the current state of the field, this work identifies major challenges and opportunities that constrain the development of robust research capabilities-particularly in the mechanistic understanding of TCM syndromes and herbal formulations, novel drug discovery, and the delivery of high-quality, patient-centered clinical care. The findings underscore that future advancements in AI-driven TCM research will rely on the development of high-quality, large-scale data repositories; the construction of comprehensive and domain-specific knowledge graphs (KGs); deeper insights into the biological mechanisms underpinning clinical efficacy; rigorous causal inference frameworks; and intelligent, personalized decision support systems.
传统中医（TCM）代表了一种个性化医疗的典范方法，它是经过2000多年临床经验数据的系统积累和完善而发展起来的，如今涵盖了大规模电子病历（EMR）和实验分子数据。自20世纪70年代以来，人工智能（AI）通过开发各种专家系统（如MYCIN）在医学领域展示了其效用。随着深度学习和大语言模型（LLMs）的出现，AI在医学领域的潜力展现出巨大前景。因此，从临床和科学角度将AI与中医相结合，是一个具有根本性和前景的研究方向。本综述对中医人工智能研究进行了深入概述，从三个角度总结了相关研究任务：系统层面生物机制阐释、真实世界临床证据推断以及个性化临床决策支持。该综述重点介绍了具有代表性的AI方法及其在中医科学探究和临床实践中的应用。为了批判性地评估该领域的当前状态，本研究确定了制约强大研究能力发展的主要挑战和机遇，特别是在对中医证候和中药配方的机理理解、新药发现以及提供高质量的以患者为中心的临床护理方面。研究结果强调，未来人工智能驱动的中医研究进展将依赖于高质量大规模数据存储库的开发、全面且特定领域知识图谱（KGs）的构建、对临床疗效背后生物机制的更深入洞察、严谨的因果推断框架以及智能的个性化决策支持系统。
